Neural Network Weight Optimization Toolbox Using Genetic Algorithms
A highly effective toolbox utilizing genetic algorithms to optimize neural network weights, addressing global convergence issues and delivering rapid training performance
Digital Beamforming of Echo Signals from 16 Array Sources
Digital beamforming of echo signals from 16 array sources, including echo signal simulation, Taylor windowing for weights, digital mixing, and beamforming processing simulation
MATLAB Implementation of Weighted Fusion Algorithm
Weighted fusion algorithm that assigns different weights to various factors to achieve optimal results, with practical MATLAB code implementation approaches.
Particle Swarm Optimization for BP Neural Network Weight Tuning
Using Particle Swarm Optimization to optimize BP neural network weights, the trained neural network is applied to fault diagnosis for pattern recognition, achieving faster convergence compared to standard BP neural networks
Genetic Algorithm Optimized Neural Network Weight Adjustment Program
MATLAB-based implementation of genetic algorithm for optimizing neural network weights with enhanced code-level descriptions
NEDI Edge Interpolation
Edge-based interpolation applying bilinear interpolation for flat regions and weighted interpolation for edge regions, using gradient information for region discrimination